Month: April 2019

How NOT to make debt collection a nightmare for your customers

Imagine a scenario where Keith (customer) and Jason (debt collector) are having a conversation:

Keith – Well yes, I know I have a balance past due, but I hope you understand this is not a good time. I am at the office right now.

Jason – Oh! I am sorry to catch you at the wrong moment; can I call back in the evening?

Keith – Well I moonlight as a Radio jokey. I can talk between either my day break 12-1 PM or 6:30 PM – 7:00 PM.

Jason – Very well, let me make a note of this……

While this looked like a perfectly executed collection call, there is a fundamental flaw in this scenario. The problem is that these notes may not be referred ever again and hence a crucial piece of information about the borrower’s communication preference would unfortunately be lost forever!

In today’s world where terabytes are no longer considered absurdly large and gigahertz is easily available on a laptop’s processors, for the lenders, not being able to use every bit of available information might mean losing out to the competition. Customer experience in debt collection is no longer just a good to have.

While the banking industry has seen a dramatic shift in the past decade or so – with online banking and app-based services that enable customers to make transactions anytime, anywhere and across devices, the debt collection process has somehow lagged behind in this customer-centric approach. Most outsourced agencies still have traditional call-center setups, which use rudimentary risk segmentation method without any regards to behavioural aspect of the customer. The lack of empathy in the collection agents’ tone coupled with untimely follow up calls may not only cause grave inconvenience to the customer but may also diminish chances of amicable collections altogether and any possibility of resurrecting customer’s loyalty towards the bank.

What banks need today is a customer centric approach to debt collection. And this is where Artificial Intelligence comes in by enabling debt collectors to effectively plan and execute a well-crafted customer segmentation strategy by utilizing data. The availability of huge volumes of historical transactions of customer data can be utilized effectively to analyze and predict patterns and create accurate risk segmentations by taking into consideration customer behaviors and outcomes.

Read our detailed whitepaper on Debt Collection made intelligent with Artificial Intelligence

In this blog I would like to propose a ‘3 RIGHTS’ strategy that uses AI to create customer friendly collection strategies.

  • Treat your customers with the RIGHT strategy

    Your customers are not apples they are oranges! and a few bad oranges do not spoil the bunch!

    In the debt collection process, a key thing is to understand that a delinquency has two dimensions. While financial ability to repay remains to be a primary dimension, ignoring willingness of the customer could lead to unpleasant experiences. A bad call with the collector could lead a highly willing customer with low financial risk, to stop co-operating with the bank or in the worst case, hold payment as a way to assert control and command attention.

    For a most efficient collection effort it thus becomes imperative to accurately measure an account on the two dimensions before assigning the right strategy. For example, accurate identification of accounts that scores high both on ability and willingness could enable collectors to assign significantly less intrusive strategies like text messages or Interactive Voice Response or for that matter not take an action, without any detrimental impact on collections. This not only saves a customer an embarrassing phone call but given that the proportion of such accounts in general is high in a delinquent portfolio, collectors save on costly calls.

    For the relatively small remainder of the delinquent portfolio, a follow-up call may be necessary and hence the next ‘2 Rights’ become relevant.

  • Know your customer’s preference for RIGHT time and channel

    There is nothing more embarrassing to a customer than to get a collection call during an office meeting. Understanding the customer preference of right time to contact is a crucial starting point to getting a promise to pay.

    Similarly, with increased access to smartphones and self-help apps, customers today prefer indirect communication modes like notifications and chatbots over a direct call when it comes to sensitive topics such as debt collection. These modes of communication can be effectively used at a time suitable for the clients (based on predictive analytics) to enhance their experience.

    Machine learning and AI can also help customize experiences for customers based on previous history or usage patterns of other customers with similar profiles. In debt collection, this could be used to interact with customers on preferred devices and at times that are most suitable to engage with them effectively.

  • Talk to your customer with RIGHT tone

    It is surprising to see that debt collection operations across the world are scantily using behavioral insights to drive their strategies for collection. Given that the collection process in many cases boils down to an artful negotiation between two individuals trying to solve a common problem by imposing the terms most beneficial to themselves.

    For most customers a phone call is an intense event. From the moment a customer picks up a debt collectors call, they are continuously making decisions on whether they would want to cooperate with the collection agent or not. A few crucial behavioral insights recommended by the machine learning models, to start the conversation on a positive note will significantly enhance customer engagement. As an example, starting the discussion with a mention of the last night’s game for a baseball fan may instantly help put the customer at ease and facilitate an amicable discussion.

    Once the call is past the initial hurdle It is primarily about matching a call script attuned to an intelligently identified behavioral profile of the customer. Such a script may be crafted with smartly personalized recommendations to provide essential hints to the collection agent to maximize the return for the lender. Some examples of these recommendations could be in terms of how long to persist until a ‘promise to pay’ is achieved or a minimum amount to accept as promised amount and so forth.

    A more sophisticated AI based system will continuously evaluate the call in real time to assess customer engagement and dynamically highlight opportunities to lead the conversation towards a promise to pay.

Conclusion

A considerable shift is the need of the hour to look at debt collection from a customer-centric perspective to bring about a change that will not only improve collections but also ensure an improved customer experience. Many banks today, are shifting to technology to ensure a customer-first approach to debt collection, and Artificial Intelligence (AI) is leading the way in ensuring effective steps to make this process efficient and profitable while ensuring higher levels of customer’s satisfaction.

Existing processes can be improved to deliver better value with the application of AI by analyzing data and making recommendations based on usage patterns. Objection handling and language used by successful agents can be analyzed to provide insights and best practices to improve performance and productivity of other agents.

Businesses thrive when customers see value in their services. A great experience with a brand is what helps customers stick. The banking industry has been actively progressing with a customer-first approach and reaping the benefits that come along. It’s time debt collection adopts this approach too, to deliver an experience that is in the interest of customers while constantly improving collection in a structured manner.

EdgeVerve’s CollectEdge is a data driven intelligence application powered by advanced machine learning that helps reduce delinquency rates, boost recoveries and improve operational efficiencies, all-the-while delivering a great customer experience.

Know More

ARE YOUR DEBT COLLECTION SYSTEMS UP-TO-DATE?

Elevating levels of debt magnify the chances of delinquency, which can considerably reduce profit margins and surge costs for lenders.

Furthermore, since lofty debt levels render the economy to extreme vulnerability, inability to check this stalemate in time can also give way to a global economic meltdown.

Thus, with the amount of global debt accumulating every year, traditional processes have been able to do very little to arrest the problem, creating the need for advanced systems that can inhibit galloping delinquency rates by also enhancing customer experience.

PROBLEMS IN TRADITIONAL COLLECTION PROCESS

In 2017, the global debt reached an all-time high of $184 trillion in nominal terms, the equivalent of 225% of GDP. On average, the world’s debt exceeded $86,000 in per capita terms, which is more than 2½ times the average income per-capita.1

To recover loans, traditional systems deploy time-intensive manual correspondences that impede proper resource utilization. Also, they only analyze recent internal transaction data and lack accurate risk segmentation models. Regular updates of data since collection or loan approval are also not available with these traditional systems.

The use of AI and machine learning (ML) has become obligatory for businesses to beef up collections and skirt the initial roadblocks presented by legacy systems.

AI IMPROVING PERFORMANCE RECORDS

When it comes to collection strategy, AI-powered machines emulate cognitive human behavior to solve a host of issues discussed below:

  • Auto-generated notifications: Automated notifications can be used to send payment notifications via SMS and e-mails that carry UPI payment options and offer the option of using debit or credit cards. Based on the best mode of communication that these intelligent machines prompt, automated notifications can also be customized. Consumer base expansion is the direct result of this. Simplifying the entire process, ML-based applications also analyze existing debtor profiles, predict the best channel and time of contacting them and schedule the type of content best suited for a response.
  • Digitized collection strategies: AI and analytics can recognize and score delinquencies based on data such as due amount, percentage outstanding and credit limit, behavioural and sentiment analysis, thus offering a more robust customer bifurcation. So, drawing up personalized collection strategies for each debtor or debtor cluster becomes convenient.
  • Identifying potential defaulters: These insights help banks identify potential instances of default and initiate action. ML and natural language processing (NLP) technologies can be used to predict customers that might be in dire financial situations and can be offered credit counseling. These offers cement customer loyalty and enhance customer experience. The modern collection system also streamlines grievance redressal processes, further bringing customer goodwill to the fore.
  • Determining costs and cash influx: Leveraging ML and analytics will help banks to determine the cost of defaults and their consequential recovery strategy. With this, costs will drop while the turnaround times will improve. In addition, AI technologies can be used to evaluate the percentage of recovery, timeliness, the process used and cash inflow.

Powered by advanced ML capabilities, CollectEdge is a data-driven intelligent application designed to help lending and collecting organizations reduce delinquency rates and boost recoveries. CollectEdge can assist companies that want to make existing debt collection systems more intelligent and seek a balance between mitigating loan losses and enhancing customer experience.

COLLECTEDGE

CollectEdge studies data across channels and looks up statutory/regional regulations to recommend appropriate channels and time for contact. It understands personality traits, negotiates and connects with borrowers based on e-mails, texts, public posts, blogs, tweets and comments. In addition, it offers preventive insights, risk predictions and ML-based queue prioritizations that focus on profiles with high chances of recovery and personalized repayment plans for delinquent customers.

Apart from major perks like lower delinquency rates, reduced charge-offs and higher operational efficiencies, the software offers a unified view of customer accounts and can be synced with existing core collection systems. With CollectEdge, companies are not required to rip and replace their existing legacy systems and train their talent resources on new technology. Easy implementation and faster time-to-market, unbiased data model and easy access for audit teams are factors that can make the product popular among companies concerned about value cycles and understandability among auditors.

Sources:

New Data on Global Debt, https://blogs.imf.org/2019/01/02/new-data-on-global-debt/, January 2, 2019

Collaborate we must in the Open World!

With the advent of technologies such as blockchain, bank regulations such as Europe’s second Payment Services Directive (PSD2) and the FinTech revolution, it is imperative to have an open mind and collaborate with the ecosystem players including competitors, third parties, FinTech players, and developers with the end objective of offering value to customers in the form of financial and non-financial offerings. As the definition of banking expands to include non-financial services, it may not be possible for banks to develop all products and services in-house and also deliver the products which the customer needs through the channel they prefer. So to remain in business, it is critical to leave behind the traditional mindset and follow new approaches to solve customer problems by enabling interactions between consumers and third party product or service providers. Unified Payments Interface (UPI) by NPCI in India is a clear example of banks participating in an ecosystem effectively. UPI-enabled banks can provide other qualified payment service providers (PSPs) connectivity to access customer account data to initiate payments. They can also allow instant transfer of funds between two bank accounts.

Similar to a cloud service provider’s offering of PaaS (Platform as a Service) banks can offer a platform that provides an infrastructure for interactions between its existing customer base and other players to offer a full suite of products and services which the customer wants thereby enabling value creation for all participants. Infosys Finacle’s 2019 Banking Trends report states that banks will arrive at different approach based on their unique vision and circumstances.

Different participants in the ecosystem will be able to access banks’ capabilities using APIs, an easy means of exchange of information in a scalable manner, enabling integration of its internal consumer data with external products and services. The platform will set control mechanisms and usage guidelines for the participants to engage and arrive at new digital offerings. As per the report, a lot more banks are expected to launch their API stores in the coming year to expose APIs to the ecosystem players including partners and developers to build real-world applications with production data.

Banks need to identify different areas of the customer journey which they want to address, and arrive at a comprehensive list of APIs. For example, in secured and unsecured lending, we can plan and arrive at a comprehensive list of APIs for different scenarios and products and not create APIs in an ad hoc manner.

Customers will also benefit in this collaborative environment. They can expect relevant financial and non-financial products and services in a single interface. They will have control over their data by providing consent and securely sharing it with the participants in the ecosystem.

Banks must think about their revenue sharing models as they embark on this customer-led business opportunity so that the ecosystem is a profitable one. Innovation with ecosystem players will be a key differentiating factor that will set one ecosystem apart from the other.

Essentials of a successful customer journey program

When we journey to a new place we are attracted by the surroundings and we like to take selfies and pictures that we can post on social media. If it’s a short trip, we want to make the most of the available time and visit more places. But if it’s a longer stay or a medium to long-term on-site assignment, travelling in that city or country, an exciting experience before, becomes a routine and mundane one now. The initial enthusiasm fades away naturally!

Similarly, in business, enterprises need to constantly look for new ways to delight their customers. Businesses must design customer journeys to weave a coherent and contextual experience at every stage. Keeping the customer at the center of any business decision rewards the business in the long run in terms of revenue generation and profitability. The customer is attracted to the initial offers like cash back on fund transfer, zero convenience fee and added benefits on digital wallets or debit/credit cards to book travel tickets/hotels/movie tickets/pay bills. Such positive experiences that intersect with customer’s profile encourage them to use more and more of the varied offerings of a business and enhance loyalty. For a retail customer it could be offering personal loan or student loan to young customers to offering an insurance product, mortgage loan, loan against securities during a later stage of life. Banks can also offer working capital loan, term loan for a manufacturing entity as per the different requirements as a customer’s business evolves and grows.

Digital objectives of any business also need to consider offering new products and services to its customers based on changing customer behavior and demands to enhance the overall experience with the engagement. This may call for the following:

  • Perform architectural changes (moving from monolithic to micro services architecture) to become more customer centric and agile.
  • Use cloud for scalability and 24X7 availability.
  • Use cloud native technology for faster deployment time, improved scalability and cloud portability.
  • Provide an intelligent app for interactive experience on customer’s smartphone that offers data-driven insights and personalized offerings which are preapproved using digital technologies like analytics, machine learning and artificial intelligence.
  • Collaborate with the ecosystem players (competitors, third parties, Fintech players, developers) through the open banking model and offer automated business-to-business interactions to exchange data at scale using APIs.
  • Listen to customer interactions on social media about one’s own brand and the competitors’.
  • Offer value to customers in the form of financial and non-financial offerings through preferred customer channels or touch points.
  • Participate in a blockchain network to enhance transparency.
  • Offer augmented reality (AR) solutions for faster clarification and resolution of problems.

All these steps will in turn offer a full suite of products, services and experiences which the customer wants today and in the future, thereby enabling value creation for all participants. Thus digital technologies ensure ease of doing business, offer instant gratification to customers and empower them for better decision making. Use of these technologies also offers faster, secure transactions end-to-end with lower transaction costs. But the choice and convenience which the businesses offer the customer should not come at the cost of their data and privacy breach.